Up until the rapid development of the Internet and the evermore increasing number of connected users, only a few best-selling products had been prevalent in various markets. This was a natural occurrence due to the limited physical space available in the stores, in which it is sensible to stock popular items. The music market was no exception: retail stores focused their sales on popular albums from the Top 100 Billboard charts. As Brynjolfsson et al. correctly predicted, product sales would be less and less concentrated, the power of balance shifting from the few best-selling products to niche products that were previously difficult for consumers to discover [2].
Indeed, with the arrival of broadband connections, lower hardware costs, and popularity of high-storage media players, the online industry has grown rapidly in the recent years. Online music stores now offer songs to users ranging in the millions, the largest online stores currently offering over 14 million songs. This availability of non-popular items creates a niche market that, collectively, could rival or exceed sales of popular items, known as the Long Tail [1].
This Long Tail business model provides consumers with millions of items to choose from, something that was not possible with retail outlets. However, offering too many choices created a problem of information overload in which, paradoxically, “consumers were less satisfied, less confident, and more confused” [5]. The solution to information overload was recommendation systems, which would ultimately filter out unnecessary items and provide only those that were relevant to the user. Among the various kinds of recommendation systems, collaborative filtering is the most successful method and most widely used in commercial services [16]. This is because collaborative filtering can be applied independent of its domain, relatively easier to implement compared to content-based and hybrid algorithms, and provides the most relevant results to users.